4:00pm to 5:50pm - Zoom - Logic Set Theory Eric Mjolsness - (UC Irvine) What does logic have to do with AI/ML for computational science? Progress in artificial intelligence (AI), including machine learning (ML), This is part 1 of a 2 part talk. It will cover background on: |
9:00am to 10:00am - Zoom - Inverse Problems Ting Zhou - (Northeastern University) Inverse Problems for Nonlinear PDEs |
10:00am to 11:00am - zoom https://uci.zoom.us/j/93076750122?pwd=Y3pLdndoQTBuNUhxQUxFMkQ2QnRFQT09 - Mathematical Physics Milivoje Lukic - (Rice University) Reflectionless canonical systems: almost periodicity and character-automorphic Fourier transforms This talk describes joint work with Roman Bessonov and Peter |
3:00pm - Zoom https://uci.zoom.us/j/99706368574 - Number Theory Remy van Dobben de Bruyn - (IAS/Princeton University) A variety that cannot be dominated by one that lifts. The recent proofs of the Tate conjecture for K3 surfaces over finite fields start by lifting the surface to characteristic 0. Serre showed in the sixties that not every variety can be lifted, but the question whether every motive lifts to characteristic 0 is open. We give a negative answer to a geometric version of this question, by constructing a smooth projective variety that cannot be dominated by a smooth projective variety that lifts to characteristic 0. |
3:00pm to 4:00pm - Zoom (notice unusual day) - Applied and Computational Mathematics Marco Donatelli - (Università dell'Insubria, Como (Italy)) Preconditioning strategies for iterative soft-thresholding algorithms Zoom
We explore the use of a regularizing preconditioner for a modification of the linearized Bregman iteration applied to the image deblurring problem with a 1-norm regularization term in the wavelet domain. Motivated by the nonstationary preconditioned iteration introduced in [1] for least-square inverse problems, we propose a new algorithm that combines this method with the linearized Bregman algorithm [2]. The proposed preconditioning strategy improves the quality of the restored images and saves some computational cost with respect to the standard preconditioning employed in the modified linearized Bregman algorithm [3] and a numerical comparison with similar methods, like FISTA, is presented. We prove that it is a regularizing and convergent method. A variant with a structure preserving preconditioner is also considered [4]. Research partly carried out with D. Bianchi, A. Buccini, Y. Cai, M. Hanke, T.Z. Huang. [1] M. Donatelli, M. Hanke, Fast nonstationary preconditioned iterative methods for ill-posed problems, with application to image deblurring, Inverse Problems, 29 (2013) 095008. [2] Y. Cai, M. Donatelli, D. Bianchi, T.Z. Huang, Regularization preconditioners for frame-based image deblurring with reduced boundary artifacts, SIAM J. Sci. Comput., 38--1 (2016), pp. B164--B189. [3] J.F. Cai, S. Osher, Z. Shen, Linearized {B}regman iterations for frame-based image deblurring, SIAM J. Imaging Sci., 2--1 (2009), pp. 226--252. [4] D. Bianchi, A. Buccini, M. Donatelli, Structure Preserving Preconditioning for Frame-Based Image Deblurring, Springer INdAM Series, 36 (2019), pp. 33--49. |